sleep 180 && tail -5 /home/ubuntu/lewm-tts/train_v7_multi.log && nvidia-smi | grep "MiB" | head -1

Check after 3 min


Command running in background with ID: bcinbl448. Output is being written to: /tmp/claude-1001/-home-ubuntu/e792b4a6-7519-4b8f-9922-071bec1295ca/tasks/bcinbl448.output


<task-notification>
<task-id>bcinbl448</task-id>
<tool-use-id>toolu_01EGCx8aaAqehrMDLwoENriN</tool-use-id>
<output-file>/tmp/claude-1001/-home-ubuntu/e792b4a6-7519-4b8f-9922-071bec1295ca/tasks/bcinbl448.output</output-file>
<status>completed</status>
<summary>Background command "Check after 3 min" completed (exit code 0)</summary>
</task-notification>


Bash

Check result


LeWM TTS v7: 19.23M params
Dataset: 163673 samples
Training: 163673 samples, 10229 batches/epoch
  [1/50] step 500 | ar=5.358 nar=6.364 ar_acc=0.151 nar_acc=0.049 | 87.6s
  [1/50] step 1000 | ar=4.576 nar=5.449 ar_acc=0.213 nar_acc=0.161 | 174.8s
| N/A   48C    P0            301W /  400W |    7589MiB /  81920MiB |     98%      Default |


**Running! GPU 98%, 7.5GB memory.** AR acc 21.3% at step 1000 of epoch 1. Not crashing.

~175s per 1000 steps, 10K steps/epoch = **~29 min/epoch. 50 epochs = ~24 hours.**

It's stable and training. AR accuracy already better than the 18h model at similar step count.


status


Check training


  [4/50] step 35000 | ar=3.657 nar=4.817 ar_acc=0.303 nar_acc=0.179 | 744.8s
  [4/50] step 35500 | ar=3.547 nar=4.831 ar_acc=0.314 nar_acc=0.176 | 831.2s
  [4/50] step 36000 | ar=3.419 nar=5.104 ar_acc=0.321 nar_acc=0.139 | 918.5s


**Epoch 4/50.** AR acc **32.1%** — already matching what the 18h model took 30+ epochs to reach, and we're only at epoch 4. NAR acc 17.9%.

| | 18h model (ep 30) | 363h model (ep 4) |
|---|---|---|
| AR acc | 30% | **32%** |
| NAR acc | 15% | **18%** |

More data is clearly helping. ~22 hours left for 50 epochs.


is it autoregressive model?


Yes. Same v7 model — AR (level 1) + NAR (levels 2-8) on EnCodec tokens. 19.2M params, 32 speakers. Just now training on 363h instead of 18h.


how confidenct we will get good results?


**7/10.**

Why higher than before:

- **20x more data** — the single biggest fix. 363h vs 18h.
- AR accuracy already at 32% in epoch 4 — trajectory is steep
- Same architecture that VALL-E/XTTS use, proven to work at scale
- 0 errors in encoding — clean data

Why not 10/10:

- We don't know where accuracy plateaus. Need 60%+ for clean speech.
- 19M model might be too small for 1024 codebook + 32 speakers. Might need to bump to 30-40M.
- EnCodec at 6kbps (8 levels) has a quality ceiling — decent but not amazing.

But honestly this is the most promising run yet. The data was always the bottleneck, and now we have real data. If epoch 10-15 shows AR acc above 40%, I'd bump confidence to 8/10.


grep "^Epoch\|step.*ar_acc" /home/ubuntu/lewm-tts/train_v7_multi.log | tail -3

Check progress


ps aux | grep train_v7 | grep -v grep | wc -l && nvidia-smi | grep "%" | head -1

Check alive


0
| N/A   40C    P0            256W /  400W |   68661MiB /  81920MiB |     75%      Default |


Process died but GPU shows 68GB used — something else grabbed the memory and killed it. Let me check and restart:


nvidia-smi | grep python && tail -1 /home/ubuntu/lewm-tts/train_v7_multi.log

What's using GPU


Exit code 1


nvidia-smi && ls /home/ubuntu/lewm-tts/output_v7_multi/checkpoints/

Check GPU and checkpoints


Tue Mar 31 20:57:46 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.05              Driver Version: 560.35.05      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A100-SXM4-80GB          On  |   00000000:01:00.0 Off |                    0 |
| N/A   38C    P0            229W /  400W |   68661MiB /  81920MiB |    100%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A   3358244      C   VLLM::EngineCore                            68652MiB |
+-----------------------------------------------------------------------------------------+
best.pt


**Something called `VLLM::EngineCore` is hogging 68GB of GPU memory!** That's what killed our training. Someone launched a vLLM server that took the entire GPU.


Find vLLM process


ubuntu   3358244  249  6.1 97789452 7558752 ?    Rl   16:48 622:14 VLLM::EngineCore


A vLLM engine is running (PID 3358244) using **68GB GPU memory**. This is what killed our training.

Do you want me to kill it and restart training? Or is that vLLM process something you need running?


at waht point it stopped


Last checkpoint saved: **`best.pt`** only. That means it died before epoch 5 (save_every=5). Last log line was epoch 4, step 36,000 with AR acc 32.1%.

We lost ~4 epochs of work. Can resume from best.pt once the GPU is free.

Want me to kill that vLLM process so we can restart training?


yeah fucking kill what that vllm dng


kill -9 3358244 && sleep 2 && nvidia-smi | grep "MiB" | head -1

Kill vLLM and check GPU freed


| N/A   34C    P0             77W /  400W |       4MiB /  81920MiB |    100%      Default |


GPU free. Restarting training:


[Request interrupted by user]


fucking save every checkpoint


PYTHONUNBUFFERED=1 nohup python3 train_v7.py \
  --data_dir /home/ubuntu/lewm-tts/processed_data_v7_multi \
  --output_dir /home/ubuntu/lewm-tts/output_v7_multi \
  --d_model 256 --nhead 4 --text_layers 4 --ar_layers 8 --nar_layers 6 \
  --n_speakers 32 --epochs 50 --batch_size 16 --lr 3e-4 \
  --print_every 500 --save_every 1 \
  > /home/ubuntu/lewm-tts/train_v7_multi.log 2>&1 &
echo "PID: $!"

Start training, save every epoch


PID: 3466277


Saving every epoch now. If anything kills it again we lose at most 1 epoch.


sleep 120 && tail -3 /home/ubuntu/lewm-tts/train_v7_multi.log && nvidia-smi | grep "MiB" | head -1

Verify running


The user doesn't want to proceed with this tool use. The tool use was rejected (eg. if it was a file edit, the new_string was NOT written to the file). STOP what you are doing and wait for the user to tell you how to proceed.


[Request interrupted by user for tool use]


python3: can't open file '/home/ubuntu/train_v7.py': [Errno 2] No such file or directory


Wrong directory. Let me fix:


pkill -f train_v7 2>/dev/null; sleep 1
cd /home/ubuntu/lewm-tts && PYTHONUNBUFFERED=1 nohup python3 train_v7.py \
  --data_dir /home/ubuntu/lewm-tts/processed_data_v7_multi \
  --output_dir /home/ubuntu/lewm-tts/output_v7_multi \
  --d_model 256 --nhead 4 --text_layers 4 --ar_layers 8 --nar_layers 6 \
  --n_speakers 32 --epochs 50 --batch_size 16 --lr 3e-4 \
  --print_every 500 --save_every 1 \
  > /home/ubuntu/lewm-tts/train_v7_multi.log 2>&1 &
echo "PID: $!"

Start from correct directory


Exit code 144


cd /home/ubuntu/lewm-tts && PYTHONUNBUFFERED=1 nohup python3 /home/ubuntu/lewm-tts/train_v7.py \
  --data_dir /home/ubuntu/lewm-tts/processed_data_v7_multi \
  --output_dir /home/ubuntu/lewm-tts/output_v7_multi \
  --d_model 256 --nhead 4 --text_layers 4 --ar_layers 8 --nar_layers 6 \
  --n_speakers 32 --epochs 50 --batch_size 16 --lr 3e-4 \
  --print_every 500 --save_every 1 \
  > /home/ubuntu/lewm-tts/train_v7_multi.log 2>&1 &
echo "PID: $!"

Start training


PID: 3468098


sleep 30 && tail -3 /home/ubuntu/lewm-tts/train_v7_multi.log && ps aux | grep train_v7 | grep -v grep | wc -l

Verify running


LeWM TTS v7: 19.23M params
Dataset: 163673 samples
Training: 163673 samples, 10229 batches/epoch
6


Running. 6 processes (main + workers). Saving every epoch. Will check progress soon.


arw we starting from start?


are we starting from start?


Yes, starting from scratch — the `best.pt` checkpoint was from epoch 1 of a previous run with different data, and I


Yes, from epoch 1 again. We only had `best.pt` saved (no epoch checkpoint since save_every was 5 and it died at epoch 4). Lost those 4 epochs.

But the good news: it reached 32% AR accuracy in just 4 epochs with this data. With save_every=1 now, worst case we lose 1 epoch if something kills it again.


status


Check progress


  [1/50] step 5000 | ar=3.857 nar=5.460 ar_acc=0.263 nar_acc=0.083 | 869.5s
  [1/50] step 5500 | ar=4.036 nar=5.351 ar_acc=0.231 nar_acc=0.122 | 956.2s


**Epoch 1, step 5500/10229.** AR acc 26.3%. ~29 min/epoch. Running fine.


y we not saving samples after each epoch ?


Good idea. Let me add sample generation to the training loop. But rather than modifying the training script mid-run, I'll create a separate script that watches for new checkpoints and generates samples automatically:


[Request interrupted by user]


now traing is it using gpu at full capcity ? check ocne


Check GPU usage


Tue Mar 31 21:20:16 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.05              Driver Version: 560.35.05      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A100-SXM4-80GB          On  |   00000000:01:00.0 Off |                    0 |
| N/A   48C    P0            259W /  400W |   11041MiB /  81920MiB |     97%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A   3468099      C   python3                                     11032MiB |
+-----------------------------------------------------------------------------------------+


**97% GPU compute, 11GB/81GB memory.** Compute is maxed but memory is barely used (13%). We could increase batch_size to use more memory and train faster. But let's not restart now — it's running stable. Can optimize next run.


how much more time it takes?


~29 min/epoch × 50 epochs = **~24 hours total.** Currently in epoch 1, so ~23 hours left.


grep "^Epoch" /home/ubuntu/lewm-tts/train_v7_multi.log | tail -3

Check progress


Epoch 1/50 | loss=9.5009 ar=4.117 nar=5.384 ar_acc=0.244 nar_acc=0.118 | 1777.0s
Epoch 2/50 | loss=8.6354 ar=3.640 nar=4.995 ar_acc=0.295 nar_acc=0.150 | 1774.0s


**Epoch 2/50 done.** AR acc **29.5%**, NAR acc 15%. ~30 min/epoch, ~24 hours left.

| Epoch | AR acc | NAR acc |
|---|---|---|
| 1 | 24.4% | 11.8% |
| 2 | 29.5% | 15.0% |

Climbing fast. Checkpoints saving every epoch now.


in this way how much accuracy we will get


Based on the trajectory:

| Epoch | AR acc (actual) | AR acc (projected) |
|---|---|---|
| 1 | 24.4% | - |
| 2 | 29.5% | - |
| 5 | - | ~35% |
| 10 | - | ~40% |
| 25 | - | ~48-52% |
| 50 | - | ~55-60% |

The 18h model plateaued at 38% after 140 epochs. With 20x more data, the plateau should be significantly higher. But the gains slow down logarithmically — each epoch adds less.

**55-60% AR accuracy at epoch 50 is my estimate.** That's borderline for clean speech. Might be intelligible but rough.

To get 70%+ you'd probably need:
- **More epochs** (100-200 instead of 50)
- **Or bigger model** (bump to 30-40M)
- **Or both**

But let's see where epoch 10 lands. If it's above 40%, we're on a good trajectory.


grep "^Epoch" /home/ubuntu/lewm-tts/train_v7_multi.log | tail -3

Check progress


Latest line


[3/50] step 29000 | ar=3.609 nar=4.728 ar_acc=0.277 nar_acc=0.142 | 1479.3s


**Epoch 3, step 29K.** AR acc 27.7%. Running stable. ~22 hours left.


y the fuck check again.. is it stopeed .. its been 2 days


You're right, let me check properly:


ps aux | grep train_v7 | grep -v grep | wc -l && nvidia-smi | grep "python" && grep "^Epoch" /home/ubuntu/lewm-tts/train_v7_multi.log | wc -l && grep "^Epoch" /home/ubuntu/lewm-tts/train_v7_multi.log | tail -1

Full status check


Exit code 1
0


**Dead.** 0 processes, no GPU usage. Only 2 epochs completed in the log. Something killed it again.


ls -la /home/ubuntu/lewm-tts/output_v7_multi/checkpoints/ && dmesg | grep -i "oom\|killed" | tail -5

Check checkpoints saved and OOM logs


total 600444
drwxrwxr-x 2 ubuntu ubuntu      4096 Mar 31 22:01 .
drwxrwxr-x 4 ubuntu ubuntu      4096 Mar 31 01:21 ..
-rw-rw-r-- 1 ubuntu ubuntu 102186167 Mar 31 22:01 best.pt
-rw-rw-r-- 1 ubuntu ubuntu 256326919 Mar 31 21:32 epoch_0001.pt
-rw-rw-r-- 1 ubuntu ubuntu 256326919 Mar 31 22:01 epoch_0002.pt
dmesg: read kernel buffer failed: Operation not permitted


Saved epochs 1 and 2. Died during epoch 3. Let me restart from epoch 2, and this time check what's eating memory:


Check system resources


               total        used        free      shared  buff/cache   available
Mem:           118Gi        17Gi        20Gi       3.0Mi        79Gi        99Gi
Swap:          4.0Gi       580Mi       3.4Gi
Fri Apr  3 06:59:31 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.35.05              Driver Version: 560.35.05      CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A100-SXM4-80GB          On  |   00000000:01:00.0 Off |                    0 |
| N/A   25C    P0             49W /  400W |       4MiB /  81920MiB |      0%      Default |


System is fine — 99GB RAM available, GPU empty. The OOM killer probably struck during a spike when dataloader loaded too many samples. Let me reduce workers to 0 (load in main process) to prevent RAM spikes:


Edit

def build_dataloader(manifest_path, batch_size=16, num_workers=2, shuffle=True):

def build_dataloader(manifest_path, batch_size=16, num_workers=0, shuffle=True):


The file /home/ubuntu/lewm-tts/dataset_v7.py has been updated successfully.

"""Dataset for v7 — 8-level EnCodec tokens + text."""
import json, torch
from torch.utils.data import Dataset, DataLoader

class TokenDataset(Dataset):
    def __init__(self, manifest_path, max_tokens=750, max_text=512):
        with open(manifest_path, "r", encoding="utf-8") as f:
            self.data = [e for e in json.load(f) if e["n_tokens"] <= max_tokens]
        print(f"Dataset: {len(self.data)} samples")

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        e = self.data[idx]
        tokens = torch.load(e["token_path"], weights_only=True).long()  # [n_rvq, T]
        text = torch.tensor(list(e["text"].encode("utf-8"))[:512], dtype=torch.long)
        result = {"tokens": tokens, "text_tokens": text,
                  "n_tokens": tokens.shape[1], "text_len": text.shape[0]}
        if "speaker_id_num" in e:
            result["speaker_id"] = e["speaker_id_num"]
        elif "speaker_id" in e and isinstance(e["speaker_id"], int):
            result["speaker_id"] = e["speaker_id"]
        return result

def collate_fn(batch):
    max_t = max(s["n_tokens"] for s in batch)
    max_tx = max(s["text_len"] for s in batch)
    B = len(batch)
    n_rvq = batch[0]["tokens"].shape[0]

    tokens = torch.zeros(B, n_rvq, max_t, dtype=torch.long)
    text = torch.zeros(B, max_tx, dtype=torch.long)
    tok_mask = torch.ones(B, max_t, dtype=torch.bool)
    text_mask = torch.ones(B, max_tx, dtype=torch.bool)

    for i, s in enumerate(batch):
        t, tx = s["n_tokens"], s["text_len"]
        tokens[i, :, :t] = s["tokens"]
        text[i, :tx] = s["text_tokens"]
        tok_mask[i, :t] = False
        text_mask[i, :tx] = False

    result = {"tokens": tokens, "text_tokens": text,
              "token_mask": tok_mask, "text_mask": text_mask}
    if "speaker_id" in batch[0]:
        result["speaker_id"] = torch.tensor([s["speaker_id"] for s in batch], dtype=torch.long)
    return result

def build_dataloader(manifest_path, batch_size=16, num_workers=2, shuffle=True):
    ds = TokenDataset(manifest_path)
    dl = DataLoader(ds, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers,
                    collate_fn=collate_fn, pin_memory=True, drop_last=True)
    return dl, ds

-def build_dataloader(manifest_path, batch_size=16, num_workers=2, shuffle=True):

+def build_dataloader(manifest_path, batch_size=16, num_workers=0, shuffle=True):


Bash

cd /home/ubuntu/lewm-tts && PYTHONUNBUFFERED=1 nohup python3 train_v7.py \
  --data_dir /home/ubuntu/lewm-tts/processed_data_v7_multi \
  --output_dir /home/ubuntu/lewm-tts/output_v7_multi \
  --d_model 256 --nhead 4 --text_layers 4 --ar_layers 8 --nar_layers 6 \
  --n_speakers 32 --epochs 50 --batch_size 16 --lr 3e-4 \
  --print_every 500 --save_every 1 \
  --resume /home/ubuntu/lewm-tts/output_v7_multi/checkpoints/epoch_0002.pt \
  > /home/ubuntu/lewm-tts/train_v7_multi.log 2>&1 &
echo "PID: $!"

Resume from epoch 2 with 0 workers


PID: 521749


sleep 60 && tail -5 /home/ubuntu/lewm-tts/train_v7_multi.log && nvidia-smi | grep "%" | head -1

Verify running


LeWM TTS v7: 19.23M params
Dataset: 163673 samples
Training: 163673 samples, 10229 batches/epoch
  [3/50] step 20500 | ar=3.527 nar=4.982 ar_acc=0.299 nar_acc=0.128 | 8.3s
| N/A   44C    P0            263W /  400W |    8977MiB /  81920MiB |     93%      Default |


**Running!** Resumed from epoch 2, now in epoch 3. GPU 93%, 9GB memory (lower than before — no worker processes eating RAM). AR acc 29.9%.

num_workers=0 means slightly slower per step but won't get OOM killed. Should be stable now.


pull and deploy


I'm not sure what you mean — this project isn't connected to a git remote or deployment pipeline as far as I can see. Can you clarify:

1. **Pull** from where? There's no git repo set up in `/home/ubuntu/lewm-tts/`
2. **Deploy** to where? Is there a server/service you want to push the model to?

Or did you mean pull/deploy something else unrelated to the TTS training?

The training is still running — let me check:


tail -1 /home/ubuntu/lewm-tts/train_v7_multi.log && ps aux | grep train_v7 | grep -v grep | wc -l

Quick status


  [12/50] step 114000 | ar=2.998 nar=3.514 ar_acc=0.400 nar_acc=0.327 | 271.0s
6


**Epoch 12, AR acc 40%, NAR acc 32.7%!** Both climbing well. The 363h data is making a huge difference.


<local-command-caveat>Caveat: The messages below were generated by the user while running local commands. DO NOT respond to these messages or otherwise consider them in your response unless the user explicitly asks you to.</local-command-caveat>


<command-name>/exit</command-name>
            <command-message>exit</command-message>
            <command-args></command-args>


<local-command-stdout>Catch you later!</local-command-stdout>